Skip to content

Current Architecture State

Latest State

Showing latest version: current-architecture-state (2026-08-31)

Current Architecture State

Executive Summary

The ea4all-agentic-build system is an agentic Architecture Intelligence Platform utilizing LangGraph, CrewAI, and specialized sub-agents to automate EA analysis and governance. Operational guides (README.md, GEMINI.md, CLAUDE.md) are well-articulated, but formal local governance persistence (AID, AEM, project ADRs) is emerging/low, leaving active orchestration code without formal project decision anchors.

Architecture Evolution Summary

The project is transitioning from operational documentation and initial code implementation toward formalized specification-driven governance. Initial Architecture Intent Document (AID) and Architecture Work items have been proposed to anchor agent boundaries and persist decision records.

Architecture Evolution

Evolutionary trajectory is moving from code-first agent graph implementation toward structured, specification-driven architecture isolation in accordance with ADR-067 evidence isolation principles.

Capability Landscape

Core active capabilities include multi-agent orchestration (LangGraph, CrewAI) and sub-agent modules (ea4all_apm, ea4all_gra, ea4all_sya, ea4all_vqa, pmo_crew, ea4all_mcp). Capabilities are operationally defined but lack project-level AID/ADR formalization.

Capability Dependencies

Sub-agent runtime relies on Python 3.12, Gradio, LangChain, and LangGraph. Governance capabilities depend on bootstrapping project-level AID and AEM governance artifacts.

Initiative Landscape

Active initiatives include 'Baseline Discovery' (Bootstrapping AID and initial AEM) and 'Governance Foundation' (Formalizing LangGraph orchestration ADR and resolving pending pattern ADRs).

Governance State

Governance readiness is 'needs_work' (confidence score 0.75). While 17 global CAS decision precedents are active, zero project-specific ADRs or AEMs are currently persisted under target project roots.

Architecture State

Hierarchical multi-agent architecture implemented in ea4all/src/graph.py using LangGraph and CrewAI. Architecture patterns follow specification-driven design but lack local ADR anchors.

Intelligence State

Architecture intelligence capabilities are provided by autonomous sub-agents executing EA model building and governance evaluation. Pattern classification framework remains tracked as emerging pending decision acceptance.

Operational State

Operational instructions and developer guides are mature and clearly defined in core docs. Operational execution relies on unpersisted multi-agent graph orchestration.

Architecture State Metrics

Total Proposed Architecture Work: 3; Proposed AIDs: 1; Proposed AEMs: 0; Alignment Findings: 3 (2 High, 1 Medium); Persisted Local ADRs: 0; Global Governance Precedents: 17.

Strategic Gaps

  1. Unpersisted Orchestration Decision: LangGraph orchestration in code lacks a project ADR. 2. Missing Intent Anchor: Target sub-agent boundaries lack project-level AID persistence. 3. Pattern Lifecycle Mismatch: Pattern classification framework is emerging while its ADR is pending.

Observed State

Observed active sub-agent implementations and operational documentation; 17 global ADRs referenced in baseline assessment; 0 local ADRs found.

Interpreted State

High operational and functional readiness exists alongside low local governance tracking readiness, raising risks of governance drift and unanchored architectural changes.

Hypothesised State

Accepting proposed Architecture Work (AID bootstrap and LangGraph ADR formalization) will elevate governance readiness to mature and enable pattern promotion.

Confidence Assessment

Medium confidence (0.75 baseline rating) derived from comprehensive alignment and baseline discovery reports, bounded by the current absence of persisted local ADRs and AEM artifacts.

Evidence Sources

baseline-assessment-ea4all-agentic-build-2026-08-31, aid-ea4all-agentic-build-2026-08-31-v1, alignment-report-ea4all-agentic-build-2026-08-31-1, aw-discovery-ea4all-agentic-build-1, aw-discovery-ea4all-agentic-build-2, aw-discovery-ea4all-agentic-build-3

Major Findings

Unpersisted LangGraph Multi-Agent Orchestration Decision

  • Confidence: HIGH
  • Evidence: alignment-report-ea4all-agentic-build-2026-08-31-1, baseline-assessment-ea4all-agentic-build-2026-08-31
  • Rationale: Active multi-agent orchestration implemented in ea4all/src/graph.py lacks a formalized, accepted project ADR under project evidence roots.

Missing Project-Level Architecture Intent Document

  • Confidence: HIGH
  • Evidence: aid-ea4all-agentic-build-2026-08-31-v1, aw-discovery-ea4all-agentic-build-1
  • Rationale: Sub-agent boundaries (ea4all_apm, ea4all_gra, ea4all_sya, ea4all_vqa, pmo_crew, ea4all_mcp) are defined in user guides but require project-level AID persistence.